tensorflow.python.framework.errors_impl.FailedPreconditionError: /; Is a directory

[jxz542189]

when i debug,a error have found, who can help me what's wrong,thanks
tf.flags.DEFINE_string("config_paths", "
../example_configs/conv_seq2seq.yml,
../example_configs/train_seq2seq.yml,
../example_configs/text_metrics_bpe.yml",
"""Path to a YAML configuration files defining FLAG
values. Multiple files can be separated by commas.
Files are merged recursively. Setting a key in these
files is equivalent to setting the FLAG value with
the same name.""")
tf.flags.DEFINE_string("hooks", "[]",
"""YAML configuration string for the
training hooks to use.""")
tf.flags.DEFINE_string("metrics", "[]",
"""YAML configuration string for the
training metrics to use.""")
tf.flags.DEFINE_string("model", "",
"""Name of the model class.
Can be either a fully-qualified name, or the name
of a class defined in seq2seq.models.""")
tf.flags.DEFINE_string("model_params", '{"vocab_source": "/root/nmt_data/toy_reverse/train/vocab.sources.txt","vocab_target": "/root/nmt_data/toy_reverse/train/vocab.targets.txt"}',
"""YAML configuration string for the model
parameters.""")

tf.flags.DEFINE_string("input_pipeline_train", '{"class": "ParallelTextInputPipelineFairseq", "params": {"source_files": "/root/nmt_data/toy_reverse/train/sources.txt", "target_files": "/root/nmt_data/toy_reverse/train/targets.txt"}}',
"""YAML configuration string for the training
data input pipeline.""")
tf.flags.DEFINE_string("input_pipeline_dev", '{"class": "ParallelTextInputPipelineFairseq", "params": {"source_files": "/root/nmt_data/toy_reverse/dev/sources.txt", "target_files": "/root/nmt_data/toy_reverse/dev/targets.txt"}}',
"""YAML configuration string for the development
data input pipeline.""")
tf.flags.DEFINE_string("buckets", None,
"""Buckets input sequences according to these length.
A comma-separated list of sequence length buckets, e.g.
"10,20,30" would result in 4 buckets:
<10, 10-20, 20-30, >30. None disabled bucketing. """)
tf.flags.DEFINE_integer("batch_size", 32,
"""Batch size used for training and evaluation.""")
tf.flags.DEFINE_string("output_dir", None,
"""The directory to write model checkpoints and summaries
to. If None, a local temporary directory is created.""")

Training parameters

tf.flags.DEFINE_string("schedule", "continuous_train_and_eval",
"""Estimator function to call, defaults to
continuous_train_and_eval for local run""")
tf.flags.DEFINE_integer("train_steps", None,
"""Maximum number of training steps to run.
If None, train forever.""")
tf.flags.DEFINE_integer("eval_every_n_steps", 1000,
"Run evaluation on validation data every N steps.")

RunConfig Flags

tf.flags.DEFINE_integer("tf_random_seed", None,
"""Random seed for TensorFlow initializers. Setting
this value allows consistency between reruns.""")
tf.flags.DEFINE_integer("save_checkpoints_secs", None,
"""Save checkpoints every this many seconds.
Can not be specified with save_checkpoints_steps.""")
tf.flags.DEFINE_integer("save_checkpoints_steps", None,
"""Save checkpoints every this many steps.
Can not be specified with save_checkpoints_secs.""")
tf.flags.DEFINE_integer("keep_checkpoint_max", 5,
"""Maximum number of recent checkpoint files to keep.
As new files are created, older files are deleted.
If None or 0, all checkpoint files are kept.""")
tf.flags.DEFINE_integer("keep_checkpoint_every_n_hours", 4,
"""In addition to keeping the most recent checkpoint
files, keep one checkpoint file for every N hours of
training.""")
tf.flags.DEFINE_float("gpu_memory_fraction", 1.0,
"""Fraction of GPU memory used by the process on
each GPU uniformly on the same machine.""")
tf.flags.DEFINE_boolean("gpu_allow_growth", False,
"""Allow GPU memory allocation to grow
dynamically.""")
tf.flags.DEFINE_boolean("log_device_placement", False,
"""Log the op placement to devices""")

[Hongwenhui-hwh]

I got the same error,did you fixed?

[donzala]

Hello,
I'm facing the same issue, please let me know how u fixed it